code: def get_mma_init_intrin(
      m_dim: int, n_dim: int, k_dim: int, dtype: str
  ) -> Tuple[PrimFunc, PrimFunc]:
      """Generator of mma init intrins"""
      del k_dim  # unused
      zero = IntImm("int32", 0).astype(dtype)
      assert (
          m_dim % 8 == 0 and n_dim % 4 == 0
      ), "m_dim and n_dim must be multiple of 8 and 4"
      assert dtype in ["float16", "float32"]
      assert (
          n_dim // 4 * int(dtype[-2:]) <= 128
      ), "n_dim vectorize failed"
  
      @T.prim_func
      def mma_init_desc(c: T.handle) -> None:
          dst = T.match_buffer(
              c,
              (m_dim, n_dim),
              dtype,
              align=64,
              offset_factor=1,
              scope="m16n8k8.matrixC",
          )
          with T.block("root"):
              T.reads()
              T.writes(dst[0:m_dim, 0:n_dim])
              for i, j in T.grid(m_dim, n_dim):
                  with T.block("init"):
                      vi, vj = T.axis.remap("SS", [i, j])
                      dst[vi, vj] = zero
  
      @T.prim_func
      def mma_init_impl(c: T.handle) -> None:
          dst = T.match_buffer(
              c,
              (m_dim, n_dim),
              dtype,
              align=64,
              offset_factor=1,
              scope="m16n8k8.matrixC",
          )
  
          with T.block("root"):
              T.reads()
              T.writes(dst[0:m_dim, 0:n_dim])
  
              for tx in T.thread_binding(0, WARP_SIZE, "threadIdx.x"):
                  for b in range(m_dim // 8):
                      for v in T.vectorized(n_dim // 4):
                          dst[
                              b * 8 + tx // 4,
                              (tx % 4) * (n_dim // 4) + v,
                          ] = zero
  
      return mma_init_desc, mma_init_impl
  
imports: from typing import Dict, Optional, Tuple, Literal
  from tvm._ffi import register_func
  from tvm.runtime import convert
  from tvm.script import tir
  from tvm.tir.function import PrimFunc
  from tvm.tir import Cast, IntImm, TensorIntrin
label_desc: Use image node_id 20 for calling a global function with example usage: get_mma_init_intrin(m_dim, n_dim, k_dim, dtype) and returns: mma_init_desc,  mma_init_impl